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A Structure-Guided Gauss-Newton Method for Shallow ReLU Neural Network
April 9, 2024, 4:42 a.m. | Zhiqiang Cai, Tong Ding, Min Liu, Xinyu Liu, Jianlin Xia
cs.LG updates on arXiv.org arxiv.org
Abstract: In this paper, we propose a structure-guided Gauss-Newton (SgGN) method for solving least squares problems using a shallow ReLU neural network. The method effectively takes advantage of both the least squares structure and the neural network structure of the objective function. By categorizing the weights and biases of the hidden and output layers of the network as nonlinear and linear parameters, respectively, the method iterates back and forth between the nonlinear and linear parameters. The …
abstract arxiv cs.lg function gauss least network neural network paper relu squares type
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